Report
TopoLa: A Universal Framework to Enhance Cell Representations for Single-cell and Spatial Omics through Topology-encoded Latent Hyperbolic Geometry
| العنوان: | TopoLa: A Universal Framework to Enhance Cell Representations for Single-cell and Spatial Omics through Topology-encoded Latent Hyperbolic Geometry |
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| المؤلفون: | Zheng, Kai, Wang, Shaokai, Xu, Yunpei, Lei, Qiming, Zhao, Qichang, Liang, Xiao, Feng, Qilong, Li, Yaohang, Li, Min, Xu, Jinhui, Wang, Jianxin |
| سنة النشر: | 2025 |
| المجموعة: | Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Genomics |
| الوصف: | Recent advances in cellular research demonstrate that scRNA-seq characterizes cellular heterogeneity, while spatial transcriptomics reveals the spatial distribution of gene expression. Cell representation is the fundamental issue in the two fields. Here, we propose Topology-encoded Latent Hyperbolic Geometry (TopoLa), a computational framework enhancing cell representations by capturing fine-grained intercellular topological relationships. The framework introduces a new metric, TopoLa distance (TLd), which quantifies the geometric distance between cells within latent hyperbolic space, capturing the network's topological structure more effectively. With this framework, the cell representation can be enhanced considerably by performing convolution on its neighboring cells. Performance evaluation across seven biological tasks, including scRNA-seq data clustering and spatial transcriptomics domain identification, shows that TopoLa significantly improves the performance of several state-of-the-art models. These results underscore the generalizability and robustness of TopoLa, establishing it as a valuable tool for advancing both biological discovery and computational methodologies. Comment: 116 pages,53 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2501.08363 |
| رقم الانضمام: | edsarx.2501.08363 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |